Abstract: An improved genetic algorithm is proposed according to lots of nonlinear programming problems found in actual mechanical design optimization. Based on the analyses of simplex search and arithmetic crossover and combining both together, an improved crossover operator is presented to improve the local searching capability of genetic algorithm and lead gradually the population to the extreme point so as to implement the rapid searching. At the same time, to lead infeasible individuals to approach the feasible region so as to improve their feasibilities for the better, the penalty and repair strategies are associated with each other to form a repair operator for repairing infeasible individuals, accelerating the speed of the individuals to approach the feasible region and improving the searching efficiency and the capability in solving the nonlinear constraint. As a whole, the performance of the algorithm is therefore improved. The validity of the algorithm proposed is verified by actual applications to nonlinear programming problems.